SoroushVahidi/scidocs huggingface dataset:
Neighborhood-Level LGBT Hate Crimes and Bullying Among Sexual Minority Youths: A Geospatial Analysis.
(2015)

The goal of this study was to evaluate a novel measure of environmental risk factors for bullying among sexual minority youths. Data on lesbian, gay, bisexual, and transgender (LGBT) assault hate crimes were obtained from police records, geocoded, and then linked to individual-level data on bullying and sexual orientation from the 2008 Boston Youth Survey Geospatial Dataset (N = 1,292; 108 sexual minorities). Results indicated that sexual minority youths who reported relational and electronic bullying were more likely to reside in neighborhoods with higher LGBT assault hate crime rates. There was no asso- ciation between LGBT assault hate crimes and bullying among heterosexual youths, pro- viding evidence for specificity to sexual minority youth. Moreover, no relationships were observed between sexual minority bullying and neighborhood-level violent and property crimes, indicating that the results were specific to LGBT assault hate crimes.
Disease and pest prediction IoT system in orchard: A preliminary study
(2017)

Each species of diseases and pests is considered to be harmful to plants and has a great adverse effect on agriculture. In addition to decreasing crop productivity and quality, pesticides or fungicides which cause environmental pollution are needed to kill diseases and pests. Therefore, the IoT system is designed to reduce the frequent use of insecticides and fungicides and to predict when the pests appear in order to lower the appearance of pests. Therefore, weather stations near the orchard have been installed and used to analyze the correlation between pests and weather data that had a large effect on pests. The prediction models have been created utilizing correlation information. In other words, we proposed a system that provides disease and pest prediction information so that farmers can quickly control them.
Mendeley: Recommendations for Researchers
(2016)

For a researcher, keeping up with what is going on in their research field can be a difficult and time-consuming task. For example, a fresh PhD student may want to know what are the relevant papers matching their research interests. An assistant professor may like to be up-to-date with what their colleagues are publishing. A professor might want to be notified about funding opportunities relevant to the work done in their research group. Since the volume of published research and research activity is constantly growing, it is becoming increasingly more difficult for researchers to be able to manage and filter through the research information flow. In this challenging context, Mendeley's mission is to become the world's "research operating system". We do this not only by providing our well-know reference management system, but also by providing discovery capabilities for researchers on different kinds of entities, such as articles and profiles. In our talk, we will share Mendeley's experiences with building our article and profile recommendation systems, the challenges that we have faced and the solutions that we have put in place. We will discuss how we address different users' needs with our data and algorithm infrastructure to achieve good user experience.
Robust Image Registration Using Log-Polar Transform
(2000)

This paper describes a hierarchical image registration algorithm for affine motion recovery. The algorithm estimates the affine transformation parameters necessary to register any two digital images misaligned due to rotation, scale, shear, and translation. The parameters are computed iteratively in a coarse-to-fine hierarchical framework using a variation of the Levenberg-Marquadt nonlinear least squares optimization method. This approach yields a robust solution that precisely registers images with subpixel accuracy. A log-polar registration module is introduced to accommodate arbitrary rotation angles and a wide range of scale changes. This serves to furnish a good initial estimate for the optimization-based affine registration stage. We demonstrate the hybrid algorithm on pairs of digital images subjected to large affine motion.
Singer Traits Identification using Deep Neural Network
(2015)

The author investigates automatic recognition of singers’ gender and age through audio features using deep neural network (DNN). Features of each singing voice, fundamental frequency and Mel-Frequency Cepstrum Coefficients (MFCC) are extracted for neural network training. 10,000 singing voice from Smule’s Sing! Karaoke app is used for training and evaluation, and the DNN-based method achieves an average recall of 91% for gender classification and 36% for age identification.
Comparing Prediction Market Structures, With an Application to Market Making
(2010)

Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Various market making algorithms have been proposed in the literature and deployed in practice, but there has been little effort to evaluate their benefits and disadvantages in a systematic manner. We introduce a novel experimental design for comparing market structures in live trading that ensures fair comparison between two different microstructures with the same trading population. Participants trade on outcomes related to a two-dimensional random walk that they observe on their computer screens. They can simultaneously trade in two markets, corresponding to the independent horizontal and vertical random walks. We use this experimental design to compare the popular inventory-based logarithmic market scoring rule (LMSR) market maker and a new information based Bayesian market maker (BMM). Our experiments reveal that BMM can offer significant benefits in terms of price stability and expected loss when controlling for liquidity; the caveat is that, unlike LMSR, BMM does not guarantee bounded loss. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.
Design and analysis of a hybrid energy harvester for self-powered sensor
(2017)

This paper presents a slightly different concept of energy harvesting method as compared to the conventional energy harvesters. The new energy harvester of interest is known as Hybrid Energy Harvester (HEH). It comprises of the Piezoelectric and Magnetostrictive/Electromagnetic transducers integrated into a system. Theoretically, the hybrid energy harvester device developed is targeted to expand the vibration bandwidth as compared to the standalone energy harvesters, hence higher output power will be generated. To develop the hybrid energy harvester, mathematical models of each respective transducers are formulated, then simulations are carried out using the MATLAB/Simulink analysis tool. Next, a macro-scale prototype device is designed, fabricated and validated. From the experimental characterizations, it is unveiled that the maximum open circuit output voltage produced by the hybrid energy harvester is 6.23 V at a resonant frequency of 59.6 Hz.
Tracking port scanners on the IP backbone
(2007)

Port scanning is the usual precursor to malicious attacks on today's Internet. Although many algorithms have been proposed for different aspects of the scan detection problem, their focused designed space is enterprise gateway level Intrusion Detection. Furthermore, we find few studies that track scanner behaviors over an extended period of time. Operating from a unique vantage point, the IP backbone, we put all the pieces together in designing and implementing a fast and accurate online port scan detection and tracking system. We introduce our flexible architecture, discuss trade-offs and design choices. Specifically, we go in depth to two design choices: the distinct counter data structure and the buffer size tuning. Our choice of a probabilistic counter is proved to have a low average error of 3.5%. The buffer size is derived using Lyapunov drift techniques on an equivalent queuing model. Our initial scanner tracking results show that the scanners' timing behavior follows a 90-10 curve. That is, 90% of scanners are active for a short time, with low scanning rates, while 10% are long term and fast scanners, with a few super-scanners lasting the entire duration of monitoring.
Evaluating enterprise resource planning ( ERP ) post-implementation problems in Egypt
(2015)

The aim of the research is to identify and evaluate the main problems experienced in the ERP postimplementation stage of multinational, privately-owned Egyptian and governmental organizations in Egypt. Data gathering was achieved by means of a set of interviews and online questionnaire conducted to 50 companies implementing ERP in Egypt. The paper presents a descriptive analysis of the difficulties and problems encountered by organizations in Egypt following ERP implementation and how these have contributed to unsuccessful implementation overall.
Prediction of Indian election using sentiment analysis on Hindi Twitter
(2016)

Sentiment analysis is considered to be a category of machine learning and natural language processing. It is used to extricate, recognize, or portray opinions from different content structures, including news, audits and articles and categorizes them as positive, neutral and negative. It is difficult to predict election results from tweets in different Indian languages. We used Twitter Archiver tool to get tweets in Hindi language. We performed data (text) mining on 42,235 tweets collected over a period of a month that referenced five national political parties in India, during the campaigning period for general state elections in 2016. We made use of both supervised and unsupervised approaches. We utilized Dictionary Based, Naive Bayes and SVM algorithm to build our classifier and classified the test data as positive, negative and neutral. We identified the sentiment of Twitter users towards each of the considered Indian political parties. The results of the analysis for Naive Bayes was the BJP (Bhartiya Janta Party), for SVM it was the BJP (Bhartiya Janta Party) and for the Dictionary Approach it was the Indian Nathional Congress. SVM predicted a 78.4% chance that the BJP would win more elections in the general election due to the positive sentiment they received in tweets. As it turned out, BJP won 60 out of 126 constituencies in the 2016 general election, far more than any other political party as the next party (the Indian National Congress) only won 26 out of 126 constituencies.
The double-edged sword of leader charisma: Understanding the curvilinear relationship between charismatic personality and leader effectiveness.
(2018)

This study advanced knowledge on charisma by (a) introducing a new personality-based model to conceptualize and assess charisma and by (b) investigating curvilinear relationships between charismatic personality and leader effectiveness. Moreover, we delved deeper into this curvilinear association by (c) examining moderation by the leader's level of adjustment and by (d) testing a process model through which the effects of charismatic personality on effectiveness are explained with a consideration of specific leader behaviors. Study 1 validated HDS charisma (Hogan Development Survey) as a useful trait-based measure of charisma. In Study 2 a sample of leaders (N = 306) were assessed in the context of a 360-degree development center. In line with the too-much-of-a-good-thing effect, an inverted U-shaped relationship between charismatic personality and observer-rated leader effectiveness was found, indicating that moderate levels are better than low or high levels of charisma. Study 3 (N = 287) replicated this curvilinear relationship and further illustrated the moderating role of leader adjustment, in such a way that the inflection point after which the effects of charisma turn negative occurs at higher levels of charisma when adjustment is high. Nonlinear mediation modeling further confirmed that strategic and operational leader behaviors fully mediate the curvilinear relationship. Leaders low on charisma are less effective because they lack strategic behavior; highly charismatic leaders are less effective because they lack operational behavior. In sum, this work provides insight into the dispositional nature of charisma and uncovers the processes through which and conditions under which leader charisma translates into (in)effectiveness. (PsycINFO Database Record
Assessment of Factors Associated with Malnutrition among Under Five Years Age Children at Machakel Woreda, Northwest Ethiopia: A CaseControl Study
(2014)

Introduction; malnutrition continues to be a significant public health and development concern not only in developing country but also in the world. It is a serious problem because it is causing the deaths of 3.5 million children under 5 years old peryear. Its magnitude is still highest in Ethiopia as well as in Amhara Region that remains a major public health problem. Objective: The main aim of this study is to assess associated factors of malnutrition on under five years children in Machakel Woreda. Methods: Unmatched Case control study was conducted. Cases were children of aged 6-59 months who have malnutrition (weight for height <2sd, weight for age ≤ 2Sd, height for age ≤ 2Sd, Mid-Upper Arm Circumstance (MUAC) <12 cm, if there is edema) and controls were 6-59 months of children those who have not malnutrition (weight for height ≥ 2sd, weight for age ≥ 2Sd, height for age ≥ 2sd, MUAC>12 cm, there is no edema). A consecutive sampling technique was employ to select study subjects for this study. Logistic regression was used to analyze data by using backward variable selection technique. Result: A total of 102 cases and 201 controls were included in the study with overall response rate of 94.4%. Sixty five (63.20%) of cases and 49 (24.40%) controls had fathers that cannot read and write. Thirty nine (38.23%) of cases and 44(21.89%) of controls had history of diarrheal episode. Those children whose family use drinking water from unprotected source were 3 times more likely to have malnutrition as compared to those children whose family use drinking water from protected source with [AOR=3.04, 95%CI (1.01, 9.17)]. Conclusion: The fining of this study revealed that inappropriate child carrying and feeding practice were strongly associated with under five malnutrition. Therefore, the responsible body should implement on nutritional intervention activities at all level of the community.
Enhancing the trust-based recommendation process with explicit distrust
(2013)

When a Web application with a built-in recommender offers a social networking component which enables its users to form a trust network, it can generate more personalized recommendations by combining user ratings with information from the trust network. These are the so-called trust-enhanced recommendation systems. While research on the incorporation of trust for recommendations is thriving, the potential of explicitly stated distrust remains almost unexplored. In this article, we introduce a distrust-enhanced recommendation algorithm which has its roots in Golbeck's trust-based weighted mean. Through experiments on a set of reviews from Epinions.com, we show that our new algorithm outperforms its standard trust-only counterpart with respect to accuracy, thereby demonstrating the positive effect that explicit distrust can have on trust-based recommendations.
Trust mechanisms for cloud computing
(2012)

Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.
A new generation of microwave bandpass filters using multilayer ring resonators
(2009)

A new generation of compact microwave bandpass filters (BPF) is proposed. The filters are based on multilayer ring resonators (MRR) technique which is applicable to both narrow band and ultra-wideband (UWB) bandpass filters. This class of filters has very compact circuit size and simple structure for fabrication. Multiple attenuation poles exist and can be simply placed to the left or right of the passband. The dimensions of the filters can be designed to around 2 mm × 2 mm × 1 mm. Out of the five filters presented, two of them are fabricated and measured. Good agreement between the simulation and measurements confirmed the design approach.
Power Amplifiers and Transmitters for RF and Microwave
(2001)

The generation of RF/microwave power is required not only in wireless communications, but also in applications such as jamming, imaging, RF heating, and miniature dc/dc converters. Each application has its own unique requirements for frequency, bandwidth, load, power, efficiency, linearity, and cost. RF power is generated by a wide variety of techniques, implementations, and active devices. Power amplifiers are incorporated into transmitters in a similarly wide variety of architectures, including linear, Kahn, envelope tracking, outphasing, and Doherty. Linearity can be improved through techniques such as feedback, feedforward, and predistortion.
CMOS Outphasing Class-D Amplifier With Chireix Combiner
(2007)

This letter presents a CMOS outphasing class-D power amplifier (PA) with a Chireix combiner. Two voltage-mode class-D amplifiers used in the outphasing system were designed and implemented with a 0.18-mum CMOS process. By applying the Chireix combiner technique, drain efficiency of the outphasing PA for CDMA signals was improved from 38.6% to 48% while output power was increased from 14.5 to 15.4 dBm with an adjacent channel power ratio of -45 dBc.
An extended Doherty amplifier with high efficiency over a wide power range
(2001)

An extension of the Doherty amplifier architecture which maintains high efficiency over a wide range of output power (>6 dB) is presented. This extended Doherty amplifier is demonstrated experimentally with InGaP-GaAs HBTs at a frequency of 950 MHz. P/sub 1 dB/ is measured at 27.5 dBm with PAE of 46%. PAE of at least 39% is maintained for over an output power range of 12 dB backed-off from P/sub 1 dB/. This is an improvement over the classical Doherty amplifier, where high efficiency is typically obtained up to 5-6 dB backed-off from P/sub 1 dB/. Generalized design equations for the Doherty amplifier are derived to show a careful choice of the output matching circuit and device scaling parameters can improve efficiencies at lower output power.
Segmenting Salient Objects from Images and Videos
(2010)

In this paper we introduce a new salient object segmentation method, which is based on combining a saliency measure with a conditional random field (CRF) model. The proposed saliency measure is formulated using a statistical framework and local feature contrast in illumination, color, and motion information. The resulting saliency map is then used in a CRF model to define an energy minimization based segmentation approach, which aims to recover well-defined salient objects. The method is efficiently implemented by using the integral histogram approach and graph cut solvers. Compared to previous approaches the introduced method is among the few which are applicable to both still images and videos including motion cues. The experiments show that our approach outperforms the current state-of-the-art methods in both qualitative and quantitative terms.
Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks
(2017)

This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology—fMRI data collected from subjects when reading affirmative or negative sentences and another one—on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real-time applications.
Locked-in syndrome : a challenge for embodied cognitive science
(2013)

Embodied approaches in cognitive science hold that the body is crucial for cognition. What this claim amounts to, however, still remains unclear. This paper contributes to its clarification by confronting three ways of understanding embodiment—the sensorimotor approach, extended cognition and enactivism—with Lockedin syndrome (LIS). LIS is a case of severe global paralysis in which patients are unable to move and yet largely remain cognitively intact. We propose that LIS poses a challenge to embodied approaches to cognition requiring them to make explicit the notion of embodiment they defend and its role for cognition. We argue that the sensorimotor and the extended functionalist approaches either fall short of accounting for cognition in LIS from an embodied perspective or do it too broadly by relegating the body only to a historical role. Enactivism conceives of the body as autonomous system and of cognition as sense-making. From this perspective embodiment is not equated with bodily movement but with forms of agency that do not disappear with body paralysis. Enactivism offers a clarifying perspective on embodiment and thus currently appears to be the framework in embodied cognition best suited to address the challenge posed by LIS.